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---
language: de
license: mit
metrics:
- accuracy
model-index:
- name: GePaBERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GePaBERT
This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on a corpus of parliamentary speeches held in the German Bundestag.
It was specifically designed for the KONVENS 2023 shared task on speaker attribution.
It achieves the following results on the evaluation set:
- Loss: 0.7997
- Accuracy: 0.8020
## Training and evaluation data
The corpus of parliamentary speeches covers speeches held in the German Bundestag during the 9th-20th legislative period, from 1980 to April 2023. (757 MB)
The speeches were automatically prepared from the publicly available [plenary protocols](https://www.bundestag.de/services/opendata), using the
extraction pipeline [Open Discourse](https://opendiscourse.de) ([GitHub code](https://github.com/open-discourse/open-discourse)).
Evaluation was done on a randomly-sampled 5% held-out dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- `learning_rate`: 2e-05
- `train_batch_size`: 8
- `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08`
- `lr_scheduler_type`: linear
- `num_epochs`: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:------:|:--------:|:---------------:|
| 1.0697 | 0.1 | 3489 | 0.7697 | 0.9802 |
| 1.0339 | 0.2 | 6978 | 0.7727 | 0.9562 |
| 1.0203 | 0.3 | 10467 | 0.7739 | 0.9463 |
| 1.0215 | 0.4 | 13956 | 0.7743 | 0.9477 |
| 1.0046 | 0.5 | 17445 | 0.7779 | 0.9299 |
| 1.0036 | 0.6 | 20934 | 0.7764 | 0.9372 |
| 1.2439 | 0.7 | 24423 | 0.7352 | 1.2473 |
| 1.4382 | 0.8 | 27912 | 0.6947 | 1.5782 |
| 1.1744 | 0.9 | 31401 | 0.7764 | 0.9360 |
| 0.9718 | 1.0 | 34890 | 0.7799 | 0.9179 |
| 0.9557 | 1.1 | 38379 | 0.7824 | 0.9038 |
| 0.947 | 1.2 | 41868 | 0.7830 | 0.9000 |
| 0.9487 | 1.3 | 45357 | 0.7833 | 0.8982 |
| 0.9457 | 1.4 | 48846 | 0.7851 | 0.8862 |
| 0.9442 | 1.5 | 52335 | 0.7863 | 0.8839 |
| 0.9473 | 1.6 | 55824 | 0.7850 | 0.8855 |
| 0.9388 | 1.7 | 59313 | 0.7865 | 0.8771 |
| 0.9293 | 1.8 | 62802 | 0.7868 | 0.8805 |
| 0.9242 | 1.9 | 66291 | 0.7873 | 0.8738 |
| 0.9241 | 2.0 | 69780 | 0.7872 | 0.8757 |
| 0.9127 | 2.1 | 73269 | 0.7896 | 0.8641 |
| 0.9114 | 2.2 | 76758 | 0.7900 | 0.8627 |
| 0.9095 | 2.3 | 80247 | 0.7913 | 0.8540 |
| 0.9042 | 2.4 | 83736 | 0.7920 | 0.8518 |
| 0.8999 | 2.5 | 87225 | 0.7919 | 0.8514 |
| 0.899 | 2.6 | 90714 | 0.7918 | 0.8543 |
| 0.8945 | 2.7 | 94203 | 0.7935 | 0.8418 |
| 0.8867 | 2.8 | 97692 | 0.7934 | 0.8437 |
| 0.893 | 2.9 | 101181 | 0.7938 | 0.8414 |
| 0.8798 | 3.0 | 104670 | 0.7951 | 0.8359 |
| 0.868 | 3.1 | 108159 | 0.7943 | 0.8375 |
| 0.8736 | 3.2 | 111648 | 0.7956 | 0.8323 |
| 0.8756 | 3.3 | 115137 | 0.7959 | 0.8315 |
| 0.8681 | 3.4 | 118626 | 0.7964 | 0.8258 |
| 0.8726 | 3.5 | 122115 | 0.7966 | 0.8266 |
| 0.8594 | 3.6 | 125604 | 0.7967 | 0.8246 |
| 0.8515 | 3.7 | 129093 | 0.7973 | 0.8227 |
| 0.8568 | 3.8 | 132582 | 0.7979 | 0.8195 |
| 0.8626 | 3.9 | 136071 | 0.7983 | 0.8173 |
| 0.8585 | 4.0 | 139560 | 0.7978 | 0.8190 |
| 0.8497 | 4.1 | 143049 | 0.7991 | 0.8127 |
| 0.8383 | 4.2 | 146538 | 0.7992 | 0.8154 |
| 0.8457 | 4.3 | 150027 | 0.8002 | 0.8080 |
| 0.8353 | 4.4 | 153516 | 0.8005 | 0.8077 |
| 0.8393 | 4.5 | 157005 | 0.8009 | 0.8027 |
| 0.8417 | 4.6 | 160494 | 0.8050 | 0.8007 |
| 0.836 | 4.7 | 163983 | 0.8004 | 0.8017 |
| 0.8317 | 4.8 | 167472 | 0.7993 | 0.8021 |
| 0.832 | 4.9 | 170961 | 0.8011 | 0.8013 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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